This paper introduces a robust, learning-based method for diagnosing the state of distribution network switchgear, which is crucial for maintaining the power quality for end users. Traditional diagnostic models often rely heavily on expert knowledge and lack robustness. To address this, our method incorporates an expanded feature vector that includes environmental data, temperature readings, switch position, motor operation, insulation conditions, and local discharge information. We tackle the issue of high dimensionality through feature mapping. The method introduces a decision radius to categorize unlabeled samples and updates the model parameters using a combination of supervised and unsupervised loss, along with a consistency regularization function. This approach ensures robust learning even with a limited number of labeled samples. Comparative analysis demonstrates that this method significantly outperforms existing models in both accuracy and robustness.

翻译：本文提出了一种鲁棒的基于学习的方法，用于诊断配电网开关设备的状态，这对维持终端用户的电能质量至关重要。传统诊断模型通常严重依赖专家知识，且缺乏鲁棒性。为解决这一问题，我们的方法引入了一个扩展的特征向量，包含环境数据、温度读数、开关位置、电机运行、绝缘状况及局部放电信息。我们通过特征映射解决了高维问题。该方法引入决策半径对未标记样本进行分类，并利用监督损失与非监督损失结合一致性正则化函数更新模型参数。这种策略确保了即使在标记样本有限的情况下也能实现鲁棒学习。对比分析表明，该方法在准确性和鲁棒性上显著优于现有模型。